How to handle class imbalancing when the actual data are that way
You can use stratified cross validation combined with an imbalanced learning technique applied to the training data. Stratification ensures that when you split your data into train and test, the ratio of frequencies between the classes will stay the same and therefore the test data will always be &quot;realistic&quot;.
However, when training a model (using only the training data, of course), the imbalance may have a negative impact. Therefore, have a look at some of the imbalanced learning techniques that are out there to remedy this situation. For example you could try these:

random undersampling: discard random examples from the majority classes until the ratios of class frequencies are close to 1
random oversampling: make random duplicates of minority class examples until the ratios of class frequencies are close to 1
SMOTE: like random oversampling, except that synthetic examples are created instead of random duplicates
balanced bagging: performs random undersampling, but does so multiple times to create an ensemble of models trained on balanced subsets of the training data

etc
You should also take care about the metrics you use to assess predictive performance on the test data. Accuracy could be misleading here, so you may instead find metrics like sensitivity and specificity (calculated for each class individually) more informative.